Administering virtual machines in a distributed computing environment

ABSTRACT

In a distributed computing environment that includes hosts that execute a VMM, where each VMM supports execution of one or more VMs, administering VMs may include: assigning, by a VMM manager, the VMMs of the distributed computing environment to a logical tree topology, including assigning one of the VMMs as a root VMM of the tree topology; and executing, amongst the VMMs of the tree topology, a broadcast operation, including: pausing, by the root VMM, execution of one or more VMs supported by the root VMM; sending, by the root VMM, to other VMMs in the tree topology, a message indicating a pending transfer of the paused VMs; and transferring the paused VMs from the root VMM to the other VMMs.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation application of and claims priorityfrom U.S. patent application Ser. No. 14/260,745, filed on Apr. 24,2014.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The field of the invention is data processing, or, more specifically,methods, apparatus, and products for administering a plurality ofvirtual machines (‘VMs’) in a distributed computing environment.

2. Description of Related Art

The development of the EDVAC computer system of 1948 is often cited asthe beginning of the computer era. Since that time, computer systemshave evolved into extremely complicated devices. Today's computers aremuch more sophisticated than early systems such as the EDVAC. Computersystems typically include a combination of hardware and softwarecomponents, application programs, operating systems, processors, buses,memory, input/output devices, and so on. As advances in semiconductorprocessing and computer architecture push the performance of thecomputer higher and higher, more sophisticated computer software hasevolved to take advantage of the higher performance of the hardware,resulting in computer systems today that are much more powerful thanjust a few years ago.

Distributed computing environments today often share and pool resourcesof many computers so that efficient utilization of resources may beeffected in order to execute various workloads. In such distributedcomputing environments many virtual machines are often instantiated toexecute workloads. Such VMs, however, from time to time requiremanagement—relocation to other hardware hosts, duplication on otherhardware hosts, failover, checkpointing, and so on. Techniques to effectsuch management operations at the present, however, lack efficiency andare often time consuming and tedious for a user to carry out. As thenumber and size of VMs in distributed computing environments increaseswith the size of the distributed computing environment itself, theinefficiencies of such management techniques also increase.

SUMMARY OF THE INVENTION

Methods, apparatus, and products for administering VMs in a distributedcomputing environment are disclosed in this specification. Such adistributed computing environment may include a plurality of hosts, withone or more of the hosts executing a virtual machine monitor (‘VMM’).Each VMM may support execution of one or more VMs. In such an embodimentadministering VMs may include assigning, by a VMM manager, the VMMs ofthe distributed computing environment to a logical tree topology,including assigning one of the VMMs as a root VMM of the tree topology.

Once assigned to a logical tree topology, administering VMs may includea executing a variety of different collective operations. In someexamples, administering VMs may include executing, amongst the VMMs ofthe tree topology, a broadcast operation, including: pausing, by theroot VMM, execution of one or more VMs supported by the root VMM;sending, by the root VMM, to other VMMs in the tree topology, a messageindicating a pending transfer of the paused VMs; and transferring thepaused VMs from the root VMM to the other VMMs.

In other examples, administering VMs may include executing, amongst theVMMs of the tree topology, a scatter operation, including: pausing, bythe root VMM one or more executing VMs; storing, by the root VMM in abuffer, a plurality of VMs to scatter amongst the other VMMs of the treetopology; and sending, by the root VMM, to each of the other VMMs of thetree topology a different one of the VMs stored in the buffer.

In yet other examples, administering VMs may include executing, amongstthe VMMs of the tree topology, a gather operation, including: sending,by the root VMM, to other VMMs in the tree topology, a request toretrieve one or more VMs supported by the other VMMs; pausing, by theother VMMs, each VM requested to be retrieved; and providing, by theother VMMs to the root VMM, the VMs requested to be retrieved.

In yet other examples, administering VMs may include executing, amongstthe VMMs of the tree topology, an allgather operation, including:sending, by the root VMM, to other VMMs in the tree topology, a requestto retrieve VMs supported by the other VMMs; pausing, by each of theother VMMs, a VM supported by the VMM; providing, by each of the otherVMMs as a response to the root VMM's request, the paused VM; andbroadcasting, by the root VM to the other VMMs as a set of VMs, thereceived VMs.

In other examples, administering VMs may include executing, by the VMMsof the tree topology, a reduce operation, including: sending, by theroot VMM to each of other VMMs of the tree topology, a request for aninstance of a particular VM; pausing, by each of the other VMMs, therequested instance of the particular VM; providing, by each of the otherVMMs to the root VMM in response to the root VMM's request, therequested instance of the particular VM; and identifying, by the rootVMM, differences among the requested instances of the particular VMincluding, performing a bitwise XOR operation amongst the instances ofthe particular VM.

The foregoing and other objects, features and advantages of theinvention will be apparent from the following more particulardescriptions of exemplary embodiments of the invention as illustrated inthe accompanying drawings wherein like reference numbers generallyrepresent like parts of exemplary embodiments of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an exemplary distributed computing environment foradministering VMs according to embodiments of the present invention.

FIG. 2 illustrates an exemplary system for administering VMs in adistributed computing environment according to embodiments of thepresent invention.

FIG. 3 sets forth a block diagram of an example compute node useful in aparallel computer capable of administering VMs in a distributedcomputing environment according to embodiments of the present invention.

FIG. 4 sets forth a block diagram of an example Point-To-Point Adapteruseful in systems for administering VMs in a distributed computingenvironment according to embodiments of the present invention.

FIG. 5 sets forth a block diagram of an example Global Combining NetworkAdapter useful in systems for administering VMs in a distributedcomputing environment according to embodiments of the present invention.

FIG. 6 sets forth a line drawing illustrating an example datacommunications network optimized for point-to-point operations useful insystems capable of administering VMs in a distributed computingenvironment according to embodiments of the present invention.

FIG. 7 sets forth a line drawing illustrating an example globalcombining network useful in systems capable of administering VMs in adistributed computing environment according to embodiments of thepresent invention.

FIG. 8 sets forth a flow chart illustrating an example method foradministering VMs in a distributed computing environment utilizing acollective broadcast operation according to embodiments of the presentinvention.

FIG. 9 sets forth a block diagram illustrating an example distributedcomputing environment in which VMs are administered utilizing acollective broadcast operation according to embodiments of the presentinvention.

FIG. 10 sets forth a flow chart illustrating an example method foradministering VMs in a distributed computing environment utilizing acollective scatter operation according to embodiments of the presentinvention.

FIG. 11 sets forth a block diagram illustrating an example distributedcomputing environment in which VMs are administered utilizing acollective scatter operation according to embodiments of the presentinvention.

FIG. 12 sets forth a flow chart illustrating an example method foradministering VMs in a distributed computing environment utilizing acollective scatterv operation according to embodiments of the presentinvention.

FIG. 13 sets forth a block diagram illustrating an example distributedcomputing environment in which VMs are administered utilizing acollective scatterv operation according to embodiments of the presentinvention.

FIG. 14 sets forth a flow chart illustrating an example method foradministering VMs in a distributed computing environment utilizing acollective gather operation according to embodiments of the presentinvention.

FIG. 15 sets forth a block diagram illustrating an example distributedcomputing environment in which VMs are administered utilizing acollective gather operation according to embodiments of the presentinvention.

FIG. 16 sets forth a flow chart illustrating an example method foradministering VMs in a distributed computing environment utilizing acollective gatherv operation according to embodiments of the presentinvention.

FIG. 17 sets forth a block diagram illustrating an example distributedcomputing environment in which VMs are administered utilizing acollective gatherv operation according to embodiments of the presentinvention.

FIG. 18 sets forth a flow chart illustrating an example method foradministering VMs in a distributed computing environment utilizing acollective allgather operation according to embodiments of the presentinvention.

FIG. 19 and FIG. 20 set forth block diagrams illustrating an exampledistributed computing environment in which VMs are administeredutilizing a collective allgather operation according to embodiments ofthe present invention.

FIG. 21 sets forth a flow chart illustrating an example method foradministering VMs in a distributed computing environment utilizing acollective reduce operation according to embodiments of the presentinvention.

FIG. 22 sets forth a flow chart illustrating a further example methodfor administering VMs in a distributed computing environment utilizing acollective reduce operation according to embodiments of the presentinvention.

FIG. 23 sets forth a block diagram illustrating an example distributedcomputing environment in which VMs are administered utilizing acollective reduce operation according to embodiments of the presentinvention.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

Exemplary methods, apparatus, and products for administering VMs in adistributed computing environment in accordance with the presentinvention are described with reference to the accompanying drawings,beginning with FIG. 1. A distributed computing environment, as the termis used in this specification, refers to a software and hardware systemin which components located on networked computers communicate andcoordinate actions with one another by passing messages. The componentsinteract with each other in order to achieve a common goal. Threesignificant characteristics of distributed computing environments are:concurrency of components, lack of a global clock, and independentfailure of components.

To that end, FIG. 1 sets forth a network diagram of a system foradministering VMs in a distributed computing environment according toembodiments of the present invention. The system of FIG. 1 includesautomated computing machinery in the form of an exemplary computer (52)useful for administering VMs in a distributed computing environmentaccording to embodiments of the present invention. The computer (52) ofFIG. 1 includes at least one computer processor (56) or ‘CPU’ as well asrandom access memory (68) (‘RAM’) which is connected through a highspeed memory bus (66) and bus adapter (58) to processor (56) and toother components of the computer (52).

Stored in RAM (68) is a virtual machine monitor (‘VMM’)(50), sometimesreferred to as a ‘hypervisor.’ The VMM (50) in the example of FIG. 1 isa module of computer program instructions that when executed by theprocessor (56) causes the computer (52) to support (or ‘run’) one ormore virtual machines (82, 84, 86). The computer upon which the VMM (50)supports VMs is referred to as a host computer.

In addition to the computer (52), the example of FIG. 1 also includesother hosts (22, 24, 26). Each of the hosts executes a separate VMM (32,40, 44). Each VMM (32, 40, 44) supports a different number of VMs (28,30, 34, 36, 38, 42).

A virtual machine, as the term is used in this specification, is alogical partition of host machine resources such that the virtualmachine is a simulation of an stand-alone, independent computer.Physical characteristics of a computing platform—computer processors,computer memory, I/O adapters, and the like—are abstracted from theperspective of an operating system and other software applications thatexecute within the virtual machine.

Each virtual machine (82, 84, 86) executed in the computer (52) of FIG.1 may support a separate operating system (16, 18, 20) and one or moreapplications (10, 12, 14). Operating systems useful in computersconfigured for administering VMs in a distributed computing environmentaccording to embodiments of the present invention include UNIX™, Linux™Microsoft XP™ AIX™ IBM's i5/OS™ and others as will occur to those ofskill in the art. The operating systems (16, 18, 20), the VMs (82, 84,86), and the VMM (50) in the example of FIG. 1 are shown in RAM (68),but many components of such software typically are stored innon-volatile memory also, such as, for example, on a disk drive (70).

The computer (52) of FIG. 1 includes disk drive adapter (72) coupledthrough expansion bus (60) and bus adapter (58) to processor (56) andother components of the computer (52). Disk drive adapter (72) connectsnon-volatile data storage to the computer (52) in the form of disk drive(70). Disk drive adapters useful in computers configured foradministering VMs in a distributed computing environment according toembodiments of the present invention include Integrated DriveElectronics (‘IDE’) adapters, Small Computer System Interface (‘SCSI’)adapters, and others as will occur to those of skill in the art.Non-volatile computer memory also may be implemented for as an opticaldisk drive, electrically erasable programmable read-only memory(so-called ‘EEPROM’ or ‘Flash’ memory), RAM drives, and so on, as willoccur to those of skill in the art.

The example computer (52) of FIG. 1 includes one or more input/output(‘I/O’) adapters (78). I/O adapters implement user-oriented input/outputthrough, for example, software drivers and computer hardware forcontrolling output to display devices such as computer display screens,as well as user input from user input devices (81) such as keyboards andmice. The example computer (52) of FIG. 1 includes a video adapter (54),which is an example of an I/O adapter specially designed for graphicoutput to a display device (80) such as a display screen or computermonitor. Video adapter (54) is connected to processor (56) through ahigh speed video bus (64), bus adapter (58), and the front side bus(62), which is also a high speed bus.

The exemplary computer (52) of FIG. 1 includes a communications adapter(67) for data communications with other computers (22, 24, 26) and fordata communications with a data communications network (100). Such datacommunications may be carried out serially through RS-232 connections,through external buses such as a Universal Serial Bus (‘USB’), throughdata communications networks such as IP data communications networks,and in other ways as will occur to those of skill in the art.Communications adapters implement the hardware level of datacommunications through which one computer sends data communications toanother computer, directly or through a data communications network.Examples of communications adapters useful in computers configured foradministering VMs in a distributed computing environment according toembodiments of the present invention include modems for wired dial-upcommunications, Ethernet (IEEE 802.3) adapters for wired datacommunications, and 802.11 adapters for wireless data communications.

In the example of FIG. 1, a server (46) executes a virtual machinemonitor manager (48). The VMM manager of FIG. 1 is a module of automatedcomputing machinery comprising computer hardware and software that isconfigured to manage the VMMs in the example of FIG. 1. Such managementmay include orchestrating virtual machine migration, failover,checkpointing, duplication, creation, deletion, edits, and deployment.Other management operations may include workload distribution amongVMMs, collection, analysis and reporting of execution statistics andenergy consumption, software and operating system update management,system health monitoring, discovery of resources in the distributedcomputing systems available for use by VMs, and so on. Such VMM managersmay also provide a user, such as a system administrator, direct controlof such management operations.

The example VMM manager (48) in may be configured to carry out VMmanagement in the distributed computing environment of FIG. 1 in variousways. Initially, the VMM manager (48) may assign the VMMs (50, 32, 40,44) of the distributed computing environment to a logical tree topology.A tree topology may include one root node logically coupled to one ormore child nodes. Those child nodes may, in turn, be logically coupledto one or more child nodes. Each node may be logically coupled to anynumber of nodes. In a binary tree topology, for example, each node(except those nodes at the ‘bottom’ of the tree) may be logicallycoupled to child nodes. In other tree topologies, such as high radixtree topologies, each node may be logically coupled to many child nodes.In assigning the VMMs of to a logical tree topology, the VMM manager(48) may also assign one of the VMMs as a root VMM of the tree topology.

Once assigned to the tree topology, the VMMs (50, 32, 40, 44) mayexecute a number of different collective operations amongst the VMMs toeffect management operations of VMs supported by the VMMs. In someembodiments, the VMMs may execute a broadcast operation by: pausing, bythe root VMM, execution of one or more VMs supported by the root VMM;sending, by the root VMM, to other VMMs in the tree topology, a messageindicating a pending transfer of the paused VMs; and transferring thepaused VMs from the root VMM to the other VMMs. Additional explanationof such a collective broadcast operation is set forth below with respectto FIGS. 8 and 9.

In other embodiments, the VMMs may execute a scatter operation by:pausing, by the root VMM one or more executing VMs; and storing, by theroot VMM in a buffer, a plurality of VMs to scatter amongst the otherVMMs of the tree topology; and sending, by the root VMM, to each of theother VMMs of the tree topology a different one of the VMs stored in thebuffer. One type of a scatter operation is a scatterv operation, and, insuch examples, sending the VMs stored in the buffer to the other VMMsmay include sending an unequal number of VMs to at least two VMMs.Additional explanation of scatter operations carried out by VMMs inaccordance with embodiments of the present invention is set forth belowwith respect to FIGS. 10 and 11, and additional explanation of scattervoperations is set forth below with respect to FIGS. 12 and 13.

In other embodiments, the VMMs may execute a gather operation by:sending, by the root VMM, to other VMMs in the tree topology, a requestto retrieve one or more VMs supported by the other VMMs; pausing, by theother VMMs, each VM requested to be retrieved; and providing, by theother VMMs to the root VMM, the VMs requested to be retrieved. One typeof gather operation is a gather operation and, in such examples,providing the VMs requested to be retrieved may include providing, by atleast one of the other VMMs, a different number of VMs than another ofthe other VMMs. Additional explanation of such gather operations is setforth below with respect to FIGS. 14 and 15 and additional explanationof gatherv operations is set forth below with respect to FIGS. 16 and17.

In other embodiments, the VMMs may execute an allgather operation by:sending, by the root VMM, to other VMMs in the tree topology, a requestto retrieve VMs supported by the other VMMs; pausing, by each of theother VMMs, a VM supported by the VMM; providing, by each of the otherVMMs as a response to the root VMM's request, the paused VM;broadcasting, by the root VM to the other VMMs as a set of VMs, thereceived VMs. Additional explanation of such an allgather operation isset forth below with respect to FIGS. 18, 19, and 20.

In yet other embodiments, the VMMs may execute a reduce operation by:sending, by the root VMM to each of other VMMs of the tree topology, arequest for an instance of a particular VM; pausing, by each of theother VMMs, the requested instance of the particular VM; providing, byeach of the other VMMs to the root VMM in response to the root VMM'srequest, the requested instance of the particular VM; and identifying,by the root VMM, differences among the requested instances of theparticular VM including, performing a bitwise XOR operation amongst theinstances of the particular VM. Additional explanation of such a reduceoperation is set forth below with respect to FIGS. 21, 22, and 23.

The arrangement of computers and other devices making up the exemplarysystem illustrated in FIG. 1 are for explanation, not for limitation.Data processing systems useful according to various embodiments of thepresent invention may include additional servers, routers, otherdevices, and peer-to-peer architectures, not shown in FIG. 1, as willoccur to those of skill in the art. Networks in such data processingsystems may support many data communications protocols, including forexample TCP (Transmission Control Protocol), IP (Internet Protocol),HTTP (HyperText Transfer Protocol), WAP (Wireless Access Protocol), HDTP(Handheld Device Transport Protocol), and others as will occur to thoseof skill in the art. Various embodiments of the present invention may beimplemented on a variety of hardware platforms in addition to thoseillustrated in FIG. 1.

One type of distributing computing environment in which VMs may beadministered according to embodiments of the present invention includesa parallel computer. Parallel computing refers to the simultaneousexecution of a task (split up and specially adapted) on multipleprocessors or multiple hardware threads to obtain results faster thanserially processing the task multiple times. Parallel computing is basedon the fact that the process of solving a problem usually can be dividedinto smaller tasks, which may be carried out simultaneously with somecoordination.

Parallel computers execute parallel algorithms or ‘parallel processes’.A parallel algorithm can be split up to be executed a piece at a time onmany different processing devices, and then put back together again atthe end to get a data processing result. Some algorithms are easy todivide up into pieces. Splitting up the job of checking all of thenumbers from one to a hundred thousand to see which are primes could bedone, for example, by assigning a subset of the numbers to eachavailable processor, and then putting the list of positive results backtogether.

In this specification, the multiple processing devices that execute theindividual pieces of a parallel program are referred to as ‘computenodes.’ A parallel computer may be composed of compute nodes and otherprocessing nodes as well, including, for example, input/output (‘I/O’)nodes, and service nodes.

Parallel algorithms are valuable because it is faster to perform somekinds of large computing tasks via a parallel algorithm than it is via aserial (non-parallel) algorithm, because of the way modern processorswork. It is far more difficult to construct a computer with a singlefast processor than one with many slow processors with the samethroughput. There are also certain theoretical limits to the potentialspeed of serial processors. On the other hand, every parallel algorithmhas a serial part and so parallel algorithms have a saturation point.After that point adding more processors does not yield any morethroughput but only increases the overhead and cost.

Parallel algorithms are designed also to optimize one more resource thedata communications requirements among the nodes of a parallel computer.There are two ways parallel processors communicate, shared memory ormessage passing. Shared memory processing needs additional locking forthe data and imposes the overhead of additional processor and bus cyclesand also serializes some portion of the algorithm.

Message passing processing uses high-speed data communications networksand message buffers, but this communication adds transfer overhead onthe data communications networks as well as additional memory need formessage buffers and latency in the data communications among nodes.Designs of parallel computers use specially designed data communicationslinks so that the communication overhead will be small but it is theparallel algorithm that decides the volume of the traffic.

Many data communications network architectures are used for messagepassing among nodes in parallel computers. Compute nodes may beorganized in a network as a ‘torus’ or ‘mesh,’ for example. Also,compute nodes may be organized in a network as a tree. A torus networkconnects the nodes in a three-dimensional mesh with wrap around links.Every node is connected to its six neighbors through this torus network,and each node is addressed by its x,y,z coordinate in the mesh. In sucha manner, a torus network lends itself to point to point operations. Ina tree network, the nodes typically are connected into a binary tree:each node has a parent, and two children (although some nodes may onlyhave zero children or one child, depending on the hardwareconfiguration). Although a tree network typically is inefficient inpoint to point communication, a tree network does provide high bandwidthand low latency for certain collective operations, message passingoperations where all compute nodes participate simultaneously, such as,for example, an allgather operation. In computers that use a torus and atree network, the two networks typically are implemented independentlyof one another, with separate routing circuits, separate physical links,and separate message buffers.

In some parallel computers, each compute node may execute one or moreparallel processes. Each parallel process may be referred to in avariety of ways depending on context including, for example, as a task,and endpoint, or a rank. In some embodiments, a single task may includemultiple endpoints, where each endpoint is a data communicationsendpoint that supports communications among many other endpoints. Insuch an embodiment a parallel process may be referred to as a singletask or alternatively as a single endpoint of a task. In some otherembodiments, each compute node may execute a single task that operatesas a single data communications endpoint. For example, a parallelcomputer that operates in accordance with the Message Passing Interface(‘MPI’) standard, described below in more detail, may execute a singlerank on each compute node of the parallel computer. In suchimplementations, a parallel process may be referred to as a rank. Alsoin such embodiments, the term task, endpoint, and rank may beeffectively synonymous.

For further explanation, FIG. 2 illustrates an exemplary system foradministering VMs in a distributed computing environment according toembodiments of the present invention. The system of FIG. 2 includes aparallel computer (100), non-volatile memory for the computer in theform of a data storage device (118), an output device for the computerin the form of a printer (120), and an input/output device for thecomputer in the form of a computer terminal (122).

The parallel computer (100) in the example of FIG. 2 includes aplurality of compute nodes (102). The compute nodes (102) are coupledfor data communications by several independent data communicationsnetworks including a high speed Ethernet network (174), a Joint TestAction Group (‘JTAG’) network (104), a global combining network (106)which is optimized for collective operations using a binary tree networktopology, and a point-to-point network (108), which is optimized forpoint-to-point operations using a torus network topology. The globalcombining network (106) is a data communications network that includesdata communications links connected to the compute nodes (102) so as toorganize the compute nodes (102) as a binary tree. Each datacommunications network is implemented with data communications linksamong the compute nodes (102). The data communications links providedata communications for parallel operations among the compute nodes(102) of the parallel computer (100).

The compute nodes (102) of the parallel computer (100) are organizedinto at least one operational group (132) of compute nodes forcollective parallel operations on the parallel computer (100). Eachoperational group (132) of compute nodes is the set of compute nodesupon which a collective parallel operation executes. Each compute nodein the operational group (132) is assigned a unique rank that identifiesthe particular compute node in the operational group (132). Collectiveoperations are implemented with data communications among the computenodes of an operational group. Collective operations are those functionsthat involve all the compute nodes of an operational group (132). Acollective operation is an operation, a message-passing computer programinstruction that is executed simultaneously, that is, at approximatelythe same time, by all the compute nodes in an operational group (132) ofcompute nodes. Such an operational group (132) may include all thecompute nodes (102) in a parallel computer (100) or a subset all thecompute nodes (102). Collective operations are often built aroundpoint-to-point operations. A collective operation requires that allprocesses on all compute nodes within an operational group (132) callthe same collective operation with matching arguments. A ‘broadcast’ isan example of a collective operation for moving data among compute nodesof an operational group. A ‘reduce’ operation is an example of acollective operation that executes arithmetic or logical functions ondata distributed among the compute nodes of an operational group (132).An operational group (132) may be implemented as, for example, an MPI‘communicator.’

‘MPI’ refers to ‘Message Passing Interface,’ a prior art parallelcommunications library, a module of computer program instructions fordata communications on parallel computers. Examples of prior-artparallel communications libraries that may be improved for use insystems configured according to embodiments of the present inventioninclude MPI and the ‘Parallel Virtual Machine’ (‘PVM’) library. PVM wasdeveloped by the University of Tennessee, The Oak Ridge NationalLaboratory and Emory University. MPI is promulgated by the MPI Forum, anopen group with representatives from many organizations that define andmaintain the MPI standard. MPI at the time of this writing is a de factostandard for communication among compute nodes running a parallelprogram on a distributed memory parallel computer. This specificationsometimes uses MPI terminology for ease of explanation, although the useof MPI as such is not a requirement or limitation of the presentinvention.

Some collective operations have a single originating or receivingprocess running on a particular compute node in an operational group(132). For example, in a ‘broadcast’ collective operation, the processon the compute node that distributes the data to all the other computenodes is an originating process. In a ‘gather’ operation, for example,the process on the compute node that received all the data from theother compute nodes is a receiving process. The compute node on whichsuch an originating or receiving process runs is referred to as alogical root.

Most collective operations are variations or combinations of four basicoperations: broadcast, gather, scatter, and reduce. The interfaces forthese collective operations are defined in the MPI standards promulgatedby the MPI Forum. Algorithms for executing collective operations,however, are not defined in the MPI standards. In a broadcast operation,all processes specify the same root process, whose buffer contents willbe sent. Processes other than the root specify receive buffers. Afterthe operation, all buffers contain the message from the root process.

A scatter operation, like the broadcast operation, is also a one-to-manycollective operation. In a scatter operation, the logical root dividesdata on the root into segments and distributes a different segment toeach compute node in the operational group (132). In scatter operation,all processes typically specify the same receive count. The sendarguments are only significant to the root process, whose bufferactually contains sendcount * N elements of a given datatype, where N isthe number of processes in the given group of compute nodes. The sendbuffer is divided and dispersed to all processes (including the processon the logical root). Each compute node is assigned a sequentialidentifier termed a ‘rank.’ After the operation, the root has sentsendcount data elements to each process in increasing rank order. Rank 0receives the first sendcount data elements from the send buffer. Rank 1receives the second sendcount data elements from the send buffer, and soon.

A gather operation is a many-to-one collective operation that is acomplete reverse of the description of the scatter operation. That is, agather is a many-to-one collective operation in which elements of adatatype are gathered from the ranked compute nodes into a receivebuffer in a root node.

A reduction operation is also a many-to-one collective operation thatincludes an arithmetic or logical function performed on two dataelements. All processes specify the same ‘count’ and the same arithmeticor logical function. After the reduction, all processes have sent countdata elements from compute node send buffers to the root process. In areduction operation, data elements from corresponding send bufferlocations are combined pair-wise by arithmetic or logical operations toyield a single corresponding element in the root process' receivebuffer. Application specific reduction operations can be defined atruntime. Parallel communications libraries may support predefinedoperations. MPI, for example, provides the following pre-definedreduction operations:

-   -   MPI_MAX maximum    -   MPI_MIN minimum    -   MPI_SUM sum    -   MPI_PROD product    -   MPI_LAND logical and    -   MPI_BAND bitwise and    -   MPI_LOR logical or    -   MPI_BOR bitwise or    -   MPI_LXOR logical exclusive or    -   MPI_BXOR bitwise exclusive or

In addition to compute nodes, the parallel computer (100) includesinput/output (‘I/O’) nodes (110, 114) coupled to compute nodes (102)through the global combining network (106). The compute nodes (102) inthe parallel computer (100) may be partitioned into processing sets suchthat each compute node in a processing set is connected for datacommunications to the same I/O node. Each processing set, therefore, iscomposed of one I/O node and a subset of compute nodes (102). The ratiobetween the number of compute nodes to the number of I/O nodes in theentire system typically depends on the hardware configuration for theparallel computer (102). For example, in some configurations, eachprocessing set may be composed of eight compute nodes and one I/O node.In some other configurations, each processing set may be composed ofsixty-four compute nodes and one I/O node. Such example are forexplanation only, however, and not for limitation. Each I/O nodeprovides I/O services between compute nodes (102) of its processing setand a set of I/O devices. In the example of FIG. 2, the I/O nodes (110,114) are connected for data communications I/O devices (118, 120, 122)through local area network (‘LAN’) (130) implemented using high-speedEthernet.

The parallel computer (100) of FIG. 2 also includes a service node (116)coupled to the compute nodes through one of the networks (104). Servicenode (116) provides services common to pluralities of compute nodes,administering the configuration of compute nodes, loading programs intothe compute nodes, starting program execution on the compute nodes,retrieving results of program operations on the compute nodes, and soon. Service node (116) runs a service application (124) and communicateswith users (128) through a service application interface (126) that runson computer terminal (122).

The parallel computer (100) of FIG. 2 operates generally foradministering VMs in a distributed computing environment in accordancewith embodiments of the present invention. One of the compute nodes inthe example of FIG. 2 may execute a VMM manager and a plurality of thecompute nodes (102) may execute a VMM that supports execution of one ormore VMs. Consider, for example, that each compute node of theoperational group (132) executes a single VMM and one or more VMs. Insuch an example, the VMM manager may assign the VMMs of the distributedcomputing environment (the compute nodes) to a logical tree topology.The VMM manager may assign one of the VMMs as a root VMM of the treetopology.

Once organized into the tree topology, the VMMs may execute one or morecollective operations to manage the VMs. Such collective operations mayinclude: a broadcast operation, a scatter operation, a scattervoperation, a gather operation, a gatherv operation, an allgatheroperation, or a reduce operation. With these collective operations, theVMMs may effectively manage migration, duplication, checkpointing, andthe like of VMs executing on compute nodes (102) of the operationalgroup (132).

Administering VMs in a distributed computing environment according toembodiments of the present invention may generally be implemented on aparallel computer that includes a plurality of compute nodes organizedfor collective operations through at least one data communicationsnetwork. In fact, such computers may include thousands of such computenodes. Each compute node is in turn itself a kind of computer composedof one or more computer processing cores, its own computer memory, andits own input/output adapters. For further explanation, therefore, FIG.3 sets forth a block diagram of an example compute node (102) useful ina parallel computer capable of administering VMs in a distributedcomputing environment according to embodiments of the present invention.The compute node (102) of FIG. 3 includes a plurality of processingcores (165) as well as RAM (156). The processing cores (165) of FIG. 3may be configured on one or more integrated circuit dies. Processingcores (165) are connected to RAM (156) through a high-speed memory bus(155) and through a bus adapter (194) and an extension bus (168) toother components of the compute node.

Stored RAM (156) is a parallel communications library (161), a libraryof computer program instructions that carry out parallel communicationsamong compute nodes, including point-to-point operations as well ascollective operations. A library of parallel communications routines maybe developed from scratch for use in systems according to embodiments ofthe present invention, using a traditional programming language such asthe C programming language, and using traditional programming methods towrite parallel communications routines that send and receive data amongnodes on two independent data communications networks. Alternatively,existing prior art libraries may be improved to operate according toembodiments of the present invention. Examples of prior-art parallelcommunications libraries include the ‘Message Passing Interface’ (‘MPI’)library and the ‘Parallel Virtual Machine’ (‘PVM’) library.

Also stored in RAM (156) of the example compute node (102) of FIG. 3 isa VMM (226) that supports execution of several VMs (228, 230, 232). Eachof the VMs executes an application (234, 236, 238) and an operatingsystem (240, 242, 244). Readers of skill in the art will recognize thateach VM may be provisioned as identical and separate instances of asingle virtual machine or be provisioned with different resources inboth kind and amount. Further, the application and operating systemexecuting in one VM may be a second instance of the same application andoperating system executing in another VM or may be a differentapplication or different operating system.

Also stored in RAM (156) of the example compute node (102) is a VMMmanager (248). Such a VMM manager is depicted in the example of FIG. 3as being executed in a compute node that also includes a VMM for ease ofexplanation. Readers of skill in the art will recognize, however, thatin other embodiments the VMM manager (246) may execute on a server ornode separate from the compute nodes (the hosts) upon which VMMs supportexecution of VMs.

The VMM manager (246) in the example of FIG. 3 may be configured foradministration of VMs in the parallel computer in accordance withembodiments of the present invention. The VMM manager (246) in theexample of FIG. 3 may assign the VMMs of the parallel computer—VMM (226)executing on compute node (102) and other compute nodes not shown inFIG. 3—to a logical tree topology, with one of the VMMs assigned as aroot VMM of the tree topology.

Once assigned to a tree topology the VMMs may carry out a number of VMadministration operations through use of collective operations. TheVMMs, for example, may execute any one of a broadcast operation, ascatter operation, a scatterv operation, a gather operation, a gathervoperation, an allgather operation, or a reduce operation to effect anyof VM migration, VM checkpointing, VM duplication, VM failover, and soon.

Also stored in RAM (156) is an operating system (162), a module ofcomputer program instructions and routines for an application program'saccess to other resources of the compute node. It is typical for anapplication program and parallel communications library in a computenode of a parallel computer to run a single thread of execution with nouser login and no security issues because the thread is entitled tocomplete access to all resources of the node. The quantity andcomplexity of tasks to be performed by an operating system on a computenode in a parallel computer therefore are smaller and less complex thanthose of an operating system on a serial computer with many threadsrunning simultaneously. In addition, there is no video I/O on thecompute node (102) of FIG. 3, another factor that decreases the demandson the operating system. The operating system (162) may therefore bequite lightweight by comparison with operating systems of generalpurpose computers, a pared down version as it were, or an operatingsystem developed specifically for operations on a particular parallelcomputer. Operating systems that may usefully be improved, simplified,for use in a compute node include UNIX™, Linux™, Windows XP™, AIX™,IBM's i5/OS™, and others as will occur to those of skill in the art.

The example compute node (102) of FIG. 3 includes several communicationsadapters (172, 176, 180, 188) for implementing data communications withother nodes of a parallel computer. Such data communications may becarried out serially through RS-232 connections, through external busessuch as USB, through data communications networks such as IP networks,and in other ways as will occur to those of skill in the art.Communications adapters implement the hardware level of datacommunications through which one computer sends data communications toanother computer, directly or through a network. Examples ofcommunications adapters useful in apparatus useful for administering VMsin a distributed computing environment include modems for wiredcommunications, Ethernet (IEEE 802.3) adapters for wired networkcommunications, and 802.11b adapters for wireless networkcommunications.

The data communications adapters in the example of FIG. 3 include aGigabit Ethernet adapter (172) that couples example compute node (102)for data communications to a Gigabit Ethernet (174). Gigabit Ethernet isa network transmission standard, defined in the IEEE 802.3 standard,that provides a data rate of 1 billion bits per second (one gigabit).Gigabit Ethernet is a variant of Ethernet that operates over multimodefiber optic cable, single mode fiber optic cable, or unshielded twistedpair.

The data communications adapters in the example of FIG. 3 include a JTAGSlave circuit (176) that couples example compute node (102) for datacommunications to a JTAG Master circuit (178). JTAG is the usual nameused for the IEEE 1149.1 standard entitled Standard Test Access Port andBoundary-Scan Architecture for test access ports used for testingprinted circuit boards using boundary scan. JTAG is so widely adaptedthat, at this time, boundary scan is more or less synonymous with JTAG.JTAG is used not only for printed circuit boards, but also forconducting boundary scans of integrated circuits, and is also useful asa mechanism for debugging embedded systems, providing a convenientalternative access point into the system. The example compute node ofFIG. 3 may be all three of these: It typically includes one or moreintegrated circuits installed on a printed circuit board and may beimplemented as an embedded system having its own processing core, itsown memory, and its own I/O capability. JTAG boundary scans through JTAGSlave (176) may efficiently configure processing core registers andmemory in compute node (102) for use in dynamically reassigning aconnected node to a block of compute nodes useful in systems foradministering VMs in a distributed computing environment to embodimentsof the present invention.

The data communications adapters in the example of FIG. 3 include aPoint-To-Point Network Adapter (180) that couples example compute node(102) for data communications to a network (108) that is optimal forpoint-to-point message passing operations such as, for example, anetwork configured as a three-dimensional torus or mesh. ThePoint-To-Point Adapter (180) provides data communications in sixdirections on three communications axes, x, y, and z, through sixbidirectional links: +x (181), −x (182), +y (183), −y (184), +z (185),and −z (186). The data communications adapters in the example of FIG. 3include a Global Combining Network Adapter (188) that couples examplecompute node (102) for data communications to a global combining network(106) that is optimal for collective message passing operations such as,for example, a network configured as a binary tree. The Global CombiningNetwork Adapter (188) provides data communications through threebidirectional links for each global combining network (106) that theGlobal Combining Network Adapter (188) supports. In the example of FIG.3, the Global Combining Network Adapter (188) provides datacommunications through three bidirectional links for global combiningnetwork (106): two to children nodes (190) and one to a parent node(192).

The example compute node (102) includes multiple arithmetic logic units(‘ALUs’). Each processing core (165) includes an ALU (166), and aseparate ALU (170) is dedicated to the exclusive use of the GlobalCombining Network Adapter (188) for use in performing the arithmetic andlogical functions of reduction operations, including an allreduceoperation. Computer program instructions of a reduction routine in aparallel communications library (161) may latch an instruction for anarithmetic or logical function into an instruction register (169). Whenthe arithmetic or logical function of a reduction operation is a ‘sum’or a ‘logical OR,’ for example, the collective operations adapter (188)may execute the arithmetic or logical operation by use of the ALU (166)in the processing core (165) or, typically much faster, by use of thededicated ALU (170) using data provided by the nodes (190, 192) on theglobal combining network (106) and data provided by processing cores(165) on the compute node (102).

Often when performing arithmetic operations in the global combiningnetwork adapter (188), however, the global combining network adapter(188) only serves to combine data received from the children nodes (190)and pass the result up the network (106) to the parent node (192).Similarly, the global combining network adapter (188) may only serve totransmit data received from the parent node (192) and pass the data downthe network (106) to the children nodes (190). That is, none of theprocessing cores (165) on the compute node (102) contribute data thatalters the output of ALU (170), which is then passed up or down theglobal combining network (106). Because the ALU (170) typically does notoutput any data onto the network (106) until the ALU (170) receivesinput from one of the processing cores (165), a processing core (165)may inject the identity element into the dedicated ALU (170) for theparticular arithmetic operation being perform in the ALU (170) in orderto prevent alteration of the output of the ALU (170). Injecting theidentity element into the ALU, however, often consumes numerousprocessing cycles. To further enhance performance in such cases, theexample compute node (102) includes dedicated hardware (171) forinjecting identity elements into the ALU (170) to reduce the amount ofprocessing core resources required to prevent alteration of the ALUoutput. The dedicated hardware (171) injects an identity element thatcorresponds to the particular arithmetic operation performed by the ALU.For example, when the global combining network adapter (188) performs abitwise OR on the data received from the children nodes (190), dedicatedhardware (171) may inject zeros into the ALU (170) to improveperformance throughout the global combining network (106).

For further explanation, FIG. 4 sets forth a block diagram of an examplePoint-To-Point Adapter (180) useful in systems for administering VMs ina distributed computing environment according to embodiments of thepresent invention. The Point-To-Point Adapter (180) is designed for usein a data communications network optimized for point-to-pointoperations, a network that organizes compute nodes in athree-dimensional torus or mesh. The Point-To-Point Adapter (180) in theexample of FIG. 4 provides data communication along an x-axis throughfour unidirectional data communications links, to and from the next nodein the −x direction (182) and to and from the next node in the +xdirection (181). The Point-To-Point Adapter (180) of FIG. 4 alsoprovides data communication along a y-axis through four unidirectionaldata communications links, to and from the next node in the −y direction(184) and to and from the next node in the +y direction (183). ThePoint-To-Point Adapter (180) of FIG. 4 also provides data communicationalong a z-axis through four unidirectional data communications links, toand from the next node in the −z direction (186) and to and from thenext node in the +z direction (185).

For further explanation, FIG. 5 sets forth a block diagram of an exampleGlobal Combining Network Adapter (188) useful in systems foradministering VMs in a distributed computing environment according toembodiments of the present invention. The Global Combining NetworkAdapter (188) is designed for use in a network optimized for collectiveoperations, a network that organizes compute nodes of a parallelcomputer in a binary tree. The Global Combining Network Adapter (188) inthe example of FIG. 5 provides data communication to and from childrennodes of a global combining network through four unidirectional datacommunications links (190), and also provides data communication to andfrom a parent node of the global combining network through twounidirectional data communications links (192).

For further explanation, FIG. 6 sets forth a line drawing illustratingan example data communications network (108) optimized forpoint-to-point operations useful in systems capable of administering VMsin a distributed computing environment according to embodiments of thepresent invention. In the example of FIG. 6, dots represent computenodes (102) of a parallel computer, and the dotted lines between thedots represent data communications links (103) between compute nodes.The data communications links are implemented with point-to-point datacommunications adapters similar to the one illustrated for example inFIG. 4, with data communications links on three axis, x, y, and z, andto and fro in six directions +x (181), −x (182), +y (183), −y (184), +z(185), and −z (186). The links and compute nodes are organized by thisdata communications network optimized for point-to-point operations intoa three dimensional mesh (105). The mesh (105) has wrap-around links oneach axis that connect the outermost compute nodes in the mesh (105) onopposite sides of the mesh (105). These wrap-around links form a torus(107). Each compute node in the torus has a location in the torus thatis uniquely specified by a set of x, y, z coordinates. Readers will notethat the wrap-around links in the y and z directions have been omittedfor clarity, but are configured in a similar manner to the wrap-aroundlink illustrated in the x direction. For clarity of explanation, thedata communications network of FIG. 6 is illustrated with only 27compute nodes, but readers will recognize that a data communicationsnetwork optimized for point-to-point operations for use in administeringVMs in a distributed computing environment in accordance withembodiments of the present invention may contain only a few computenodes or may contain thousands of compute nodes. For ease ofexplanation, the data communications network of FIG. 6 is illustratedwith only three dimensions, but readers will recognize that a datacommunications network optimized for point-to-point operations for usein administering VMs in a distributed computing environment inaccordance with embodiments of the present invention may in facet beimplemented in two dimensions, four dimensions, five dimensions, and soon. Several supercomputers now use five dimensional mesh or torusnetworks, including, for example, IBM's Blue Gene Q™.

For further explanation, FIG. 7 sets forth a line drawing illustratingan example global combining network (106) useful in systems capable ofadministering VMs in a distributed computing environment according toembodiments of the present invention. The example data communicationsnetwork of FIG. 7 includes data communications links (103) connected tothe compute nodes so as to organize the compute nodes as a tree. In theexample of FIG. 7, dots represent compute nodes (102) of a parallelcomputer, and the dotted lines (103) between the dots represent datacommunications links between compute nodes. The data communicationslinks are implemented with global combining network adapters similar tothe one illustrated for example in FIG. 5, with each node typicallyproviding data communications to and from two children nodes and datacommunications to and from a parent node, with some exceptions. Nodes inthe global combining network (106) may be characterized as a physicalroot node (202), branch nodes (204), and leaf nodes (206). The physicalroot (202) has two children but no parent and is so called because thephysical root node (202) is the node physically configured at the top ofthe binary tree. The leaf nodes (206) each has a parent, but leaf nodeshave no children. The branch nodes (204) each has both a parent and twochildren. The links and compute nodes are thereby organized by this datacommunications network optimized for collective operations into a binarytree (106). For clarity of explanation, the data communications networkof FIG. 7 is illustrated with only 31 compute nodes, but readers willrecognize that a global combining network (106) optimized for collectiveoperations for use in administering VMs in a distributed computingenvironment in accordance with embodiments of the present invention maycontain only a few compute nodes or may contain thousands of computenodes.

In the example of FIG. 7, each node in the tree is assigned a unitidentifier referred to as a ‘rank’ (250). The rank actually identifies atask or process that is executing a parallel operation according toembodiments of the present invention. Using the rank to identify a nodeassumes that only one such task is executing on each node. To the extentthat more than one participating task executes on a single node, therank identifies the task as such rather than the node. A rank uniquelyidentifies a task's location in the tree network for use in bothpoint-to-point and collective operations in the tree network. The ranksin this example are assigned as integers beginning with 0 assigned tothe root tasks or root node (202), 1 assigned to the first node in thesecond layer of the tree, 2 assigned to the second node in the secondlayer of the tree, 3 assigned to the first node in the third layer ofthe tree, 4 assigned to the second node in the third layer of the tree,and so on. For ease of illustration, only the ranks of the first threelayers of the tree are shown here, but all compute nodes in the treenetwork are assigned a unique rank.

VM Administration Utilizing Broadcast Operations

For further explanation, FIG. 8 sets forth a flow chart illustrating anexample method for administering VMs in a distributed computingenvironment utilizing a collective broadcast operation according toembodiments of the present invention. The method of FIG. 8 may becarried out in a distributed computing environment similar to that setforth in the example system of FIG. 1 and the example parallel computerof FIG. 2. Such a distributed computing environment may include aplurality of hosts, with one or more of the hosts executing a VMM. Insuch an environment, each VMM may support execution of one or more VMs.

The method of FIG. 8 includes assigning (802), by a VMM manager, theVMMs of the distributed computing environment to a logical treetopology. In the example of FIG. 1, assigning (802) VMMs to a logicaltree topology also includes assigning one of the VMMs as a root VMM ofthe tree topology.

Assigning (802) VMMs to a logical tree topology may be carried out invarious ways. In some embodiments, the assignment may be carried outbased on physical network characteristics of the hosts upon which theVMMs execute. That is, the VMM manager may assign VMMs asparent-children pairs when such the hosts of those VMMs are closelycoupled, physically, for network communications (e.g. shortest hops,fewest interceding network devices, closest physical distance, and thelike). The VMM manager may record such assignments and each VMM'srelationship to parent and child VMMs in the tree in a data structure.The VMM manager may then provide the data structure to each of the VMMsin the logical tree topology or may provide, separately to each VMM,only the parent and child relationships for that VMM. Readers of skillin the art will recognize that these are just a few ways, among manypossible ways, in which a VMM manager may assign VMMs to a logical treetopology.

The method of FIG. 8 also includes executing (804), amongst the VMMs ofthe tree topology, a broadcast operation. As mentioned above, abroadcast operation is generally effected when data is sent from theroot of a logical tree to all other nodes in the logical tree. In theexample of FIG. 8, executing (804) the broadcast operation includespausing (806), by the root VMM, execution of one or more VMs supportedby the root VMM; sending (808), by the root VMM, to other VMMs in thetree topology, a message indicating a pending transfer of the pausedVMs; and transferring (810) the paused VMs from the root VMM to theother VMMs. Here, the data broadcast from the root VMM to other VMMs ofthe logical tree topology is one or more VMs supported by the root VMM.

Pausing (806) execution of the VMs may be carried out in various ways,including, for example, by the VMM manager raising an interrupt or anexception in the process or processes that effect the execution of theVMs to be paused. From the perspective of the VM, the VM's hostprocessor is stopped. Because such a processor is, in fact, asimulation, the operation of which can be controlled by the VMM manager,the VMM manager may effectively hold execution of a VM withoutsimulating a power-off in the virtual machine or booting the virtualmachine down.

The root VMM may send (808), to other VMMs in the tree topology, amessage indicating a pending transfer of the paused VMs in a variety ofways. In some embodiments, each VMM is configured with a ‘mailbox’ ormessage queue established and designated for the sole use of receivingmessages from the root or other VMMs in the logical tree. The message inthe example of FIG. 8 may include an identification of an upcomingtransfer of data in the form of VMs and the size of the VMs to betransferred, and a logical identifier of the memory space in which tostore the VMs (a ‘handle’). Responsive to such a message, the other VMMsmay allocate memory space using the provided handle and in the sizeindicated in the message.

The root VMM may transfer (810) the paused VMs to the other VMMs of thelogical tree topology in a variety of ways. In some embodiments, theroot VMM may transfer the paused VMs in multiple data communicationsmessages. In some embodiments, the distributed computing environment isa parallel computer, the hosts upon which the VMMs are executing arecompute nodes of the parallel computer, each of the compute nodes isconfigured with a plurality of communications adapters, and eachcommunications adapter is configured to couple the compute node to othercompute nodes for data communications through one of a plurality of datacommunications networks. In such a parallel computer, transferring (810)the paused VMs may be carried out via one or more of the networks. Insome examples, the networks include a multi-dimensional, point-to-pointnetwork and a global combining network similar to those set forth in theexample of FIGS. 3, 4, 5, 6, and 7. In a point-to-point network, forexample, Direct Memory Access (DMA) operations including either remoteGET or direct PUT operations, may be utilized to transfer the paused VMsfrom a memory location in the root VMM directly to a memory location inthe children of the root VMM and in the same fashion down the tree,until all VMMs of the tree topology have received or retrieved the data.

In the example of FIG. 8, the broadcast operation may be executed (804)at various times. In some embodiments, executing (804) the broadcastoperation may be carried out by executing (818) the broadcast operationperiodically at predefined intervals without user intervention.Consider, for example, that at a particular time in the evening everyday, all VMs of the root VMM are migrated through use of the broadcastoperation to the non-root VMMs.

In other embodiments, executing (804) the broadcast operation may becarried out by executing (820) the broadcast operation on-demandresponsive to a user request. For example, the VMM manager may provide agraphical user interface or other interface means by which a user, suchas a system administrator, may request a migration or distributedcheckpoint of the VMs executing under control or supported by aparticular VMM.

In yet other embodiments, executing (804) the broadcast operation may becarried out by executing (822) the broadcast operation, automaticallywithout user intervention, in dependence upon an execution policy (824)and responsive to an event specified in the execution policy. Anexecution policy, as the term is used in this specification, refers to aspecification of one or more actions to take upon an occurrence of oneor more events. Consider, for example, an execution policy thatspecifies a duplication of all the VMs on the root VMM if the workloadexecuting within the VMs of the root VMM has not completed withinparticular amount of time. Consider, as another example, an executionpolicy that specifies migration of VMs from the root VMM to all otherVMs upon the processor of the root VMM's host machine reaching aparticular temperature.

VMs may be transferred from the root VMM to the other VMMs in thelogical tree for a variety of purposes including duplication,distributed checkpointing, migration and the like. To that end, themethod of FIG. 8 also includes executing (812), upon receipt by theother VMMs, the transferred VMs and resuming (813), by the root VMM,execution of the paused VMs. In this way, execution of the VMs isduplicated on all VMMs in the logical tree including the root VMM.

The method of FIG. 8 also includes not resuming (814) execution of thepaused VMs by the root VMM, and executing (815), upon receipt by theother VMMs, the transferred VMs. In this way, the VMs are migrated fromthe root VMM to the non-root VMMs and execution continues on thenon-root VMMs from the point at which the VMs were paused.

The method of FIG. 8 also includes storing (816), upon receipt by theother VMMs, the transferred VMs without executing the transferred VMs.In this way, a copy of the VMs, paused at a particular point duringexecution, may be provided to all non-root VMMs and stored as acheckpoint (or ‘backup’) from which another VMM may restore execution ata future time. Further, the checkpoint is distributed to many VMMsthereby reducing risk of losing the checkpoint data due to failure hostmachine failure.

For further explanation, FIG. 9 sets forth a block diagram illustratingan example distributed computing environment in which VMs areadministered utilizing a collective broadcast operation according toembodiments of the present invention. The example environment of FIG. 9includes six VMMs (VMM₀-VMM₅) which are assigned to a logical treetopology with one VMM (VMM₀) being assigned as a root and all other VMMsbeing assigned as a child of the root.

The root VMM (VMM₀) in the example of FIG. 9 supports execution of sixVMs (VM₀-VM₅). The VMMs in the example of FIG. 9 may execute a broadcastoperation by the root VMM (VMM₀) pausing execution of one or more VMs(VM₀-VM₅) supported by the root VMM; sending, by the root VMM, to otherVMMs in the tree topology, a message indicating a pending transfer ofthe paused VMs; and transferring the paused VMs (VM₀-VM₅) from the rootVMM to the other VMMs. In the example of FIG. 9, all VM's supported bythe root are transferred to all VMMs in the logical tree topology. Afterall VMMs in the logical tree topology have received the transferred VMs,the VMs may be executed (for migration or duplication) or stored (forcheckpointing).

VM Administration Utilizing Scatter Operations

For further explanation, FIG. 10 sets forth a flow chart illustrating anexample method for administering VMs in a distributed computingenvironment utilizing a collective scatter operation according toembodiments of the present invention. The method of FIG. 10 is similarto the method of FIG. 8 in that the method of FIG. 10 may be carried outin a similar distributed computing environment. Further, the method ofFIG. 10 includes assigning (1002), by a VMM manager, the VMMs of thedistributed computing environment to a logical tree topology, includingassigning one of the VMMs as a root VMM of the tree topology. Assigningthe VMMs of the distributed computing environment may be carried out asdescribed above with respect to a similar assignment (802) in FIG. 8.

The method of FIG. 10 differs from the method of FIG. 8, however, inthat the method of FIG. 10 includes executing (1004), amongst the VMMsof the tree topology, a scatter operation. In the method of FIG. 10,executing (1004) a scatter operation includes: pausing (1006), by theroot VMM one or more executing VMs; storing (1008), by the root VMM in abuffer, a plurality of VMs to scatter amongst the other VMMs of the treetopology; and sending (1010), by the root VMM, to each of the other VMMsof the tree topology a different one of the VMs stored in the buffer.

Pausing (1006) the VMs may be carried out as described above. Storing(1008) the plurality of VMs to scatter in a buffer may be carried out byallocating a buffer with a number of elements equal to the number ofVMMs in the tree topology less one for the root, where each element isequal to or greater that the size of the largest VM to be scattered.Then, in each element of the buffer, the root VM may store a differentVM to be scattered. Such a buffer may be allocated in a number ofdifferent data structures, including, for example, a linked list. Insome embodiments, storing (1008) a VM in an element of the buffer may becarried out by storing a pointer to the memory location of the VM in anelement, so that the buffer itself may remain relatively small.

In the method of FIG. 10, sending (1010), to each non-root VMM, adifferent VM stored in the buffer may be carried out in a variety ofways. In some embodiments, the sending (1010) may be carried out with aplurality of data communications message, each message sent to anon-root VMM with a portion of the data forming a VM. In anotherembodiment, the root VMM may send (1010) a different VM to each non-rootVMM by sending a pointer and an offset to the non-root VMM. The pointermay point to a memory location in the root VMM's memory space for thebeginning memory address of the VM and the offset indicates the entiresize of the VM such that the non-root VMM may retrieve the VM from theroot VMM's memory directly.

Further, as mentioned above with respect to FIG. 8, the distributedcomputing environment may be implemented as a parallel computer withcompute nodes operating as hosts of the VMMs and the VMs, where each ofthe compute nodes include a plurality of communications adaptersconfigured to couple the compute nodes for data communications through aplurality of different networks. In such a parallel computer, sending(1010) the VMs may be carried out via one or more of the networks. Insome examples, the networks include a multi-dimensional, point-to-pointnetwork and a global combining network similar to those set forth in theexample of FIGS. 3, 4, 5, 6, and 7. In a point-to-point network, forexample, Direct Memory Access (DMA) operations including either remoteGET or direct PUT operations, may be utilized to send the VMs from amemory location in the root VMM directly to a memory location in thechildren of the root VMM and in the same fashion down the tree, untilall VMMs of the tree topology have received or retrieved the data.

Executing (1004) the scatter operation in the example of FIG. 10 may becarried out at various times or for various reasons. In the method ofFIG. 10, for example, executing (1004) the scatter operation includesexecuting (1018) the scatter operation periodically at predefinedintervals without user intervention; executing (1020) the scatteroperation on-demand responsive to a user request; or executing (1022)the scatter operation, automatically without user intervention, independence upon an execution policy (1024) and responsive to an eventspecified in the execution policy.

VMs may be sent from the root to non-root VMMs in a scatter operationfor a variety of purposes including duplication, distributedcheckpointing, migration and the like. To that end, the method of FIG.10 includes executing (1012), upon receipt by the other VMMs, thetransferred VMs. Once each of the VMMs receives a different VM andexecutes, the VMs have effectively been migrated to the other VMMS orexecution has been duplicated on the other VMMs. Whether migration orduplication has been effected is determined in dependence upon theactions of the root VMM after sending (1010) the VMs to the non-rootVMMs. In FIG. 10, for example, after transferring the VMs, the root VMMdoes not resume (1014) execution of ht epaused VMMs. In such an example,the scatter operation effects a migration of the VMs from the root VMMto the non-root VMMs. In other embodiments in which all VMMs in the treeexecute the VMs, the scatter operation effects duplication of executionof the VMs. That is, in such an embodiment, each VM originally executingon the root VMM executes on two VMMs (the root and one other VMM) afterthe scatter operation.

The method of FIG. 10 also includes storing (1016), upon receipt by theother VMMs, the transferred VMs without executing the transferred VMs.In this way, each non-root VMM that receives a VM operates as arepository for a checkpoint of that VM.

FIG. 11 sets forth a block diagram illustrating an example distributedcomputing environment in which VMs are administered utilizing acollective scatter operation according to embodiments of the presentinvention. The example environment of FIG. 11 includes six VMMs(VMM₀-VMM₅) which are assigned to a logical tree topology with one VMM(VMM₀) being assigned as a root and all other VMMs being assigned as achild of the root.

The root VMM (VMM₀) in the example of FIG. 11 supports execution of sixVMs (VM₀-VM₅). The VMMs in the example of FIG. 11 may execute a scatteroperation by: pausing, by the root VMM one or more executing VMs(VM₁-VM₅); storing, by the root VMM in a buffer (1102), a plurality ofVMs (VM₁-VM₅) to scatter amongst the other VMMs (VMM₁-VMM₅) of the treetopology; and sending, by the root VMM (VMM₀), to each of the other VMMs(VMM₁-VMM₅) of the tree topology a different one of the VMs stored inthe buffer. In FIG. 11, for example, the root VMM sends VM₁ to VMM₁, VM₂to VMM₂, VM₃ to VMM₃, VM₄ to VMM₄, and VM₅ to VMM₅. By sending adifferent VM to each VMM, the VMMs effect a scatter operation(contrasted with a broadcasted operation where all VMMs receives allVMs).

One type of scatter operation is a scatterv (referring to ‘variable’)operation. To that end, FIG. 12 sets forth a flow chart illustrating anexample method for administering VMs in a distributed computingenvironment utilizing a collective scatterv operation according toembodiments of the present invention. The method of FIG. 12 is similarto the method of FIG. 10 in that the method of FIG. 12 includesassigning (1002) the VMMs to a logical tree topology and executing(1004) a scatter operation. The example method of FIG. 12 differs fromthe method of FIG. 10, however, in that in the method of FIG. 12,executing (1004), amongst the VMMs of the tree topology, a scatteroperation includes executing (1202), amongst the VMMs of the treetopology, a scatterv operation. In sending (1010) a different one of theVMs stored in the buffer to each of the other VMMs to effect thescatterv operation, the method of FIG. 12 includes sending (4) anunequal number of VMs to at least two VMMs. In such a scattervoperation, the root VMM may effect a migration of VMs in terms ofpriority. That is, the root VMM may send the highest priority VMs to themost powerful (in terms of computer hardware or software resources)host, while the root VMM sends lower priority VMs to slower hardwarehosts and the like. The root VMM may also couple organize the VMs intogroups based on workload type and send (1204) the groups of VMs todifferent VMMs. These are but a few reasons to effect a scattervoperation with the VMMs. Readers of skill in the art will recognize thatthere may be many reasons.

For further explanation, FIG. 13 sets forth a block diagram illustratingan example distributed computing environment in which VMs areadministered utilizing a collective scatterv operation according toembodiments of the present invention. The example environment of FIG. 12includes six VMMs (VMM₀-VMM₅) which are assigned to a logical treetopology with one VMM (VMM₀) being assigned as a root and all other VMMsbeing assigned as a child of the root.

The root VMM (VMM₀) in the example of FIG. 11 supports execution ofeight VMs (VM₀-VM₇). The VMMs in the example of FIG. 11 may execute ascatterv operation by: pausing, by the root VMM one or more executingVMs (VM₁-VM₇); storing, by the root VMM in a buffer (1302), a pluralityof VMs (VM₁-VM₇) to scatter amongst the other VMMs (VMM₁-VMM₅) of thetree topology; and sending by the root VMM (VMM₀), an unequal number ofVMs to at least two VMMs. In the example of FIG. 13, the root VMM, inaccordance with the filter or criteria (1304), sends VM₁ to VMM₁, VM₂and VM₃ to VMM₂, VM₄ and VM₅ to VMM₃, VM₆ to VMM₄, and VM₇ to VMM₅.

VM Administration Utilizing Gather Operations

FIG. 14 sets forth a flow chart illustrating an example method foradministering VMs in a distributed computing environment utilizing acollective gather operation according to embodiments of the presentinvention. The method of FIG. 14 is similar to the method of FIG. 8 inthat the method of FIG. 14 may be carried out in a similar distributedcomputing environment and includes assigning (1402), by a VMM manager,the VMMs of the distributed computing environment to a logical treetopology, including assigning one of the VMMs as a root VMM of the treetopology. Assigning (1402) the VMMs of the distributed computingenvironment may be carried out as described above with respect to asimilar assignment (802) in FIG. 8.

The method of FIG. 14 differs from the method of FIG. 8, however, inthat the method of FIG. 14 includes executing (1404), amongst the VMMsof the tree topology, a gather operation. In the method of FIG. 14,executing (1404) the gather operation includes: sending (1406), by theroot VMM, to other VMMs in the tree topology, a request to retrieve oneor more VMs supported by the other VMMs; pausing (1408), by the otherVMMs, each VM requested to be retrieved; and providing (1410), by theother VMMs to the root VMM, the VMs requested to be retrieved.

The distributed computing environment in which the example method ofFIG. 14 is carried out may be implemented as a parallel computer. Such aparallel computer may include a plurality of compute nodes, with each ofthe compute nodes operating as one of the plurality of hosts andexecuting at least one of the VMMs. Each of the compute nodes may alsoinclude plurality of communications adapters, with each communicationsadapter configured to couple the compute node to other compute nodes fordata communications and to one of a plurality of data communicationsnetworks. In some embodiments, the networks may include amulti-dimensional, point-to-point network and a global combiningnetwork. In such embodiments, providing (1410) the requested VMs to theroot VMM may be carried out through use of the point-to-point network,the global combining network, some combination of the two, with DMAaccess, through messaging, or any number of other ways as will occur toreaders of skill in the art.

In the method of FIG. 14, executing (1404) the gather operation may becarried out at various times. Executing (1404) the gather operation may,for example, include executing (1418) the gather operation periodicallyat predefined intervals without user intervention. Executing (1404) thegather operation may also include executing (1420) the gather operationon-demand responsive to a user request. Executing (1404) the gatheroperation may also include executing (1422) the gather operation,automatically without user intervention, in dependence upon an executionpolicy (1424) and responsive to an event specified in the executionpolicy.

Once the requested VMs have been provided to the root VMM, the method ofFIG. 14 includes executing (1412), by the root VMM, the provided VMsupon receipt. In this way, the gather operation effects of a many-to-onemigration or duplication of execution of the provided VMs. Inembodiments in which the source VMMs resume execution of the VMs afterproviding the VMs to the root VMM and the root VMM executes the receivedVMs, execution of the VMs is duplicated. By contrast, the method of FIG.14 also includes not resuming (1414) execution of the provided VMs bythe VMMs other than the root after providing the VMs. In this way, thegather operation effects a many-to-one migration of VMs to the root VMM.

The method of FIG. 14 also includes storing (1416), upon receipt of theprovided VMs, the provided VMs without executing the provided VMs. Inthis way, the root VM operates a repository for checkpoints of VMsexecuting on other VMMs. In such an embodiment, the pausing (1408) ofthe VM to may last only long enough to store a copy of the VM to provideto the root VMM, then execution may be immediately restored.

For further explanation, FIG. 15 sets forth a block diagram illustratingan example distributed computing environment in which VMs areadministered utilizing a collective gather operation according toembodiments of the present invention. The example environment of FIG. 15includes six VMMs (VMM₀-VMM₅) which are assigned to a logical treetopology with one VMM (VMM₀) being assigned as a root and all other VMMsbeing assigned as a child of the root.

The root VMM (VMM₀) in the example of FIG. 15 supports execution of oneVM (VM₀) prior to a gather operation with each of the other VMMssupporting execution of a different, single VM. The VMMs in the exampleof FIG. 15 may execute a gather operation by: sending, by the root VMM(VMM₀), to other VMMs in the tree topology, a request to retrieve one ormore VMs (VM₁-VM₅) supported by the other VMMs; pausing, by the otherVMMs (VMM₁-VMM₅), each VM requested to be retrieved; and providing, bythe other VMMs (VMM₁-VMM₅) to the root VMM (VMM₀), the VMs requested tobe retrieved. The root VMM (VMM₀) stores the received VMs in a buffer(1502). VMM₁ provides VM₁ to the root, VMM₂ provides VM₂ to the root,VMM₃ provides VM₃ to the root, VMM₄ provides VM₄ to the root, and VMM₅provides VM₅ to the root.

One type of gather operation is a gatherv operation in which differentcounts (size or number of discrete components) of data may be retrievedfrom different sources. For further explanation, therefore, FIG. 16 setsforth a flow chart illustrating an example method for administering VMsin a distributed computing environment utilizing a collective gathervoperation according to embodiments of the present invention. The methodof FIG. 16 is similar to the method of FIG. 14 in that the method ofFIG. 16 also includes assigning (1402) the VMMs to a logical treetopology and executing (1404) a gather operation. The method of FIG. 16differs from the method of FIG. 16, however, in that in the method ofFIG. 16 executing (1404) the gather operation is carried out byexecuting (1602) a gatherv operation and providing (1410) the VMsrequested to be retrieved is carried out by providing (1604), by atleast one of the other VMMs, a different number of VMs than another ofthe other VMMs.

For further explanation, FIG. 17 sets forth a block diagram illustratingan example distributed computing environment in which VMs areadministered utilizing a collective gatherv operation according toembodiments of the present invention. The example environment of FIG. 17includes four VMMs (VMM₀-VMM₃) which are assigned to a logical treetopology with one VMM (VMM₀) being assigned as a root and all other VMMsbeing assigned as a child of the root.

The root VMM (VMM₀) in the example of FIG. 15 supports execution of oneVM (VM₀) prior to a gatherv operation with each of the other VMMssupporting execution of a different number of VMs. The VMMs in theexample of FIG. 15 may execute a gather operation by: sending, by theroot VMM (VMM₀), to other VMMs in the tree topology, a request toretrieve one or more VMs (VM₁-VM₅) supported by the other VMMs; pausing,by the other VMMs (VMM₁-VMM₃), each VM requested to be retrieved; andproviding, by the other VMMs (VMM₁-VMM₃) to the root VMM (VMM₀), the VMsrequested to be retrieved. The root VMM (VMM₀) stores the received VMsin a buffer (1502). In the example of FIG. 17, VMM₁ provides VM₁ to theroot, VMM₂ provides VM₂ and VM₃ to the root, VMM₃ provides VM₄ and VM₅to the root. The selection of the VMs to be provided may be carried outin various ways. For example, the root VMM, in sending the request toretrieve VMs may uniquely identify those VMs requested for retrieval. Inother embodiments, the root VMM requests VMs for retrieval without anyknowledge of the number of VMs that will be provided by any one of theVMMs. Then, in dependence upon the a filter or pre-defined criteria(1702), each VMM may separately identify VMs supported by that VMM toprovide as a response to the request for retrieval. Such filter orcriteria may be user specified, static, dynamically modified, and mayinclude any type of criteria that may be utilized to select a number ofVMs among a plurality of VMs.

VM Administration Utilizing Allgather Operations

FIG. 18 sets forth a flow chart illustrating an example method foradministering VMs in a distributed computing environment utilizing acollective allgather operation according to embodiments of the presentinvention. The method of FIG. 18 is similar to the method of FIG. 8 inthat the method of FIG. 18 may be carried out in a similar distributedcomputing environment. Further, the method of FIG. 18 includes assigning(1802), by a VMM manager, the VMMs of the distributed computingenvironment to a logical tree topology, including assigning one of theVMMs as a root VMM of the tree topology. Assigning the VMMs of thedistributed computing environment may be carried out as described abovewith respect to a similar assignment (802) in FIG. 8.

The method of FIG. 18 differs from the method of FIG. 18, however, inthat the method of FIG. 18 includes executing (1804), amongst the VMMsof the tree topology, an allgather operation. In the method of FIG. 18,executing (1804) the allgather operation includes: sending (1806), bythe root VMM, to other VMMs in the tree topology, a request to retrieveVMs supported by the other VMMs; pausing (1808), by each of the otherVMMs, a VM supported by the VMM; providing (1810), by each of the otherVMMs as a response to the root VMM's request, the paused VM; andbroadcasting (1826), by the root VM to the other VMMs as a set of VMs,the received VMs. In this way, a set of all VMs executing on all VMMs inthe logical tree topology may be gather and then provided to all VMMs.At the completion of such an allgather operation each VMM is inpossession of the same VMs as all other VMs in the logical treetopology.

As described above with the other collective operations, the distributedcomputing environment in which the method of FIG. 18 is carried out maybe implemented as a parallel computer. Such a parallel computer mayinclude a plurality of compute nodes, with each of the compute nodesoperating as one of the plurality of hosts and executing at least one ofthe VMMs. Each of the compute nodes may also include plurality ofcommunications adapters, with each communications adapter configured tocouple the compute node to other compute nodes for data communicationsand to one of a plurality of data communications networks. In someembodiments, the networks may include a multi-dimensional,point-to-point network and a global combining network. In suchembodiments, providing (1810) the paused VMs and broadcasting (1826) thereceived VMs to the root VMM may be carried out through use of thepoint-to-point network, the global combining network, some combinationof the two, with DMA access, through messaging, or any number of otherways as will occur to readers of skill in the art.

The allgather operation of FIG. 18 may be carried out at various times.In the method of FIG. 18, for example, executing (1804) the allgatheroperation may include executing (1818) the allgather operationperiodically at predefined intervals without user intervention;executing (1820) the allgather operation on-demand responsive to a userrequest; or executing (1822) the allgather operation, automaticallywithout user intervention, in dependence upon an execution policy (1824)and responsive to an event specified in the execution policy.

After the broadcast (1826) of the set of VMs, the VMMs may take variousactions. The method of FIG. 18, for example, includes executing the setof VMs by all VMMs (including the root) after the receipt of the set ofVMs broadcasted by the root VMM. In this way, the VMs are gathered,broadcast as a set, and execution of the VMs is duplicated in amany-to-many relationship among all VMMs in the logical tree topology.

The method of FIG. 18 also includes storing (1814), by all VMMs, each ofthe set of VMs without executing the set of VMs after receiving the setof VMs broadcasted by the root VMM. In this way, a checkpoint of each VMin the set of VMs are distributed across the entire logical tree.

The method of FIG. 18 also includes executing (1816), only by the rootVMM, each of the set of VMs after broadcasting the set of VMs. In thisway, the non-root VMMs migrate execution of the VMs to the root VMMwhile storing a checkpoint for each of the set of VMs at each VMM.

The method of FIG. 18 also includes executing (1828), only by VMMs otherthan the root VMM, each of the set of VMs after receiving the set ofVMs. In this way, the root VMM operates as a repository of checkpointsfor each of the set of VMs while the non-root VMMs operate to duplicateexecution of the VMMs throughout the logical tree (on all but the rootVMM). Duplication provides an opportunity for a “race to finish”environment, where a high priority workload may be duplicated on manydifferent host machines and operating environments. Such an environmentenables the workload to be executed in the shortest amount of timepossible, without knowledge as to which host is actually going toprovide the quickest execution.

For further explanation, FIG. 19 and FIG. 20 set forth block diagramsillustrating an example distributed computing environment in which VMsare administered utilizing a collective allgather operation according toembodiments of the present invention.

FIG. 21 sets forth a flow chart illustrating an example method foradministering VMs in a distributed computing environment utilizing acollective reduce operation according to embodiments of the presentinvention. The example environment of FIG. 17 includes six VMMs(VMM₀-VMM₅) which are assigned to a logical tree topology with one VMM(VMM₀) being assigned as a root and all other VMMs being assigned as achild of the root.

The VMMs (VMM₀-VMM₅) in the example of FIG. 15 each support execution ofa different VM (VM₀₋VM₅). The VMMs in the example of FIG. 15 may executean allgather operation by: sending, by the root VMM, to other VMMs inthe tree topology, a request to retrieve VMs supported by the otherVMMs; pausing, by each of the other VMMs, a VM supported by the VMM; andproviding, by each of the other VMMs as a response to the root VMM'srequest, the paused VM. The remainder of the allgather operation isdescribed below with respect to FIG. 20. In FIG. 20, the root VMM(VMM₀), after receiving the VMs from the non-root VMMs, broadcasts, tothe other VMMs as a set of VMs, the received VMs. In this way, each VMMhas a copy of all VMs in the logical tree topology with correspondingVMs being paused at exactly the same point in execution.

VM Administration Utilizing Reduce Operations

FIG. 21 sets forth a flow chart illustrating an example method foradministering VMs in a distributed computing environment utilizing acollective reduce operation according to embodiments of the presentinvention.

The method of FIG. 21 is similar to the method of FIG. 8 in that themethod of FIG. 21 may be carried out in a similar distributed computingenvironment and includes assigning (1402), by a VMM manager, the VMMs ofthe distributed computing environment to a logical tree topology,including assigning one of the VMMs as a root VMM of the tree topology.Assigning (2102) the VMMs of the distributed computing environment maybe carried out as described above with respect to a similar assignment(802) in FIG. 8.

The method of FIG. 21 differs from the method of FIG. 8, however, inthat the method of FIG. 21 includes executing (2104), by the VMMs of thetree topology, a reduce operation. In the method of FIG. 21, executing(2104) the reduce operation includes: sending (2106), by the root VMM toeach of other VMMs of the tree topology, a request for an instance of aparticular VM. Each of the VMMs may execute any number of VMs and inmany cases the VMs may be completely different—different provisioning ofresources, different operating systems, different, applications orworkloads, and the like. In some embodiments, however, many differentVMMs may support execution of the same VM, with a different instance ofthat VM executed by each different VMM.

To that end, executing (2104) the reduce operation continues by pausing(2108), by each of the other VMMs, the requested instance of theparticular VM and providing (2110), by each of the other VMMs to theroot VMM in response to the root VMM's request, the requested instanceof the particular VM. Pausing VMs is described above in greater detail.Providing (2110) the requested instance of the particular VM to the rootVMM may be carried out in a variety of ways, some of which may depend onthe implementation of the distributed computing environment in which theVMMs and VMs operate. In some embodiments, the distributed computingenvironment in which the example method of FIG. 21 is carried out may beimplemented as a parallel computer. Such a parallel computer may includea plurality of compute nodes, with each of the compute nodes operatingas one of the plurality of hosts and executing at least one of the VMMs.Each of the compute nodes may also include plurality of communicationsadapters, with each communications adapter configured to couple thecompute node to other compute nodes for data communications and to oneof a plurality of data communications networks. In some embodiments, thenetworks may include a multi-dimensional, point-to-point network and aglobal combining network. In such embodiments, providing (2110) therequested instance of the particular VM to the root VMM may be carriedout through use of the point-to-point network, the global combiningnetwork, some combination of the two, with DMA access, throughmessaging, or any number of other ways as will occur to readers of skillin the art.

Executing (2104) the reduce operation then continues by identifying(2126), by the root VMM, differences among the requested instances ofthe particular VM. In the example of FIG. 21, identifying (2126)differences among the requested instances of the particular VM iscarried out by performing (2128) a bitwise XOR (exclusive OR) operationamongst the instances of the particular VM. In some embodiments, eachcompute node in the logical tree topology may include an ALU (arithmeticlogic unit) configured to execute such bitwise XOR operations withoututilizing primary CPU resources of the compute node. A bitwise XOR takestwo bit patterns, typically of equal length, and performs a logicalexclusive OR operation on each pair of corresponding bits. The result ineach position is 1 if only the first bit is 1 or only the second bit is1, but will be 0 if both are 0 or both are 1. In this way, the bitwiseXOR compares two bits and results in a 1 if the two bits are differentand a 0 if the two bits are the same.

Executing (2104) the reduce operation may be carried out at varioustimes. In the method of FIG. 21, for example, executing (2104) thereduce operation may include executing (2118) the reduce operationperiodically at predefined intervals without user intervention;executing (2120) the reduce operation on-demand responsive to a userrequest; and executing (2122) the reduce operation, automaticallywithout user intervention, in dependence upon an execution policy (2124)and responsive to an event specified in the execution policy.

For further explanation, FIG. 22 sets forth a flow chart illustrating afurther example method for administering VMs in a distributed computingenvironment utilizing a collective reduce operation according toembodiments of the present invention. The method of FIG. 22 is similarto the method of FIG. 21 in that the method of FIG. 22 includesassigning (2102) the VMMs to a logical tree topology and executing(2104) the reduce operation, including, among other elements of theexecution, identifying differences among the requested instances of theparticular VM by performing a bitwise XOR operation amongst theinstances of the particular VM.

The method of FIG. 22 differs from the method of FIG. 21, however, inthat the method of FIG. 22 includes establishing (2202), by the root VMMfor each of the other VMMs, a checkpoint of the instance of theparticular VM. Although two different instances of the same VM executingon two different hosts and supported by two different VMMs may executedifferently (at different rates, with different outcomes, and so on), itis likely that a large portion of the data forming the two separateinstances is identical. In a system with 100 VMMs, each executing aseparate instance of a particular VM, storing 100 checkpoints for eachinstance may require a large amount of memory space. Instead, the methodof FIG. 22, as described below in greater detail, provides a means bywhich checkpoints for many instances of the same VM may utilize memoryspace much more efficiently.

Establishing (2202) a checkpoint for each instance of the particular VMincludes retrieving (2204), by the root VMM from the other VMMs, memorypages that include the differences identified through the bitwise XORoperation and storing (2206) the root VMM's instance of the particularVM along with the memory pages retrieved from each of the other VMMs.The root VMM's instance of the particular VM is stored in whole as abase image of the particular VM. Then, for each of the other instances,only memory pages that include differences from the base image arestored.

To that end, the method of FIG. 22 also includes restarting (2208), fromthe checkpoint by a VMM other than the root VMM, an instance of theparticular VM. In the method of FIG. 22, restarting an instance of theparticular VM from the checkpoint may be carried out by: retrieving(2210), from the root VMM, memory pages retrieved from the other VMM(those memory pages include differences from the base image); retrieving(2212), from the root VMM, the root VMM's stored instance of theparticular VM (the base image); modifying the root VMM's stored instanceof the particular VM with the retrieved memory pages; and executing(2216) the modified instance of the particular VM.

FIG. 23 sets forth a block diagram illustrating an example distributedcomputing environment in which VMs are administered utilizing acollective reduce operation according to embodiments of the presentinvention. The example environment of FIG. 23 includes six VMMs(VMM₀-VMM₅) which are assigned to a logical tree topology with one VMM(VMM₀) being assigned as a root and all other VMMs being assigned as achild of the root.

Each VMM in the example of FIG. 23 supports a separate instance of aparticular VM (VM₀ _(—) ₀-VM₀ _(—) ₅). The VMMs in the example of FIG.23 may execute a reduce operation by: sending, by the root VMM to eachof other VMMs of the tree topology, a request for an instance of theparticular VM (VM₀ _(—) ₁-VM₀ _(—) ₅); pausing, by each of the otherVMMs (VMM₁-VMM₅), the requested instance of the particular VM;providing, by each of the other VMMs to the root VMM in response to theroot VMM's request, the requested instance of the particular VM; andidentifying, by the root VMM, differences (2304) among the requestedinstances of the particular VM. In the example of FIG. 23, each instanceof the particular VM received from a non-root VMM is compared, via abitwise XOR operation (2302), to the instance of the particular VM (VM₀_(—) ₀) supported by the root VMM (VMM₀). In this way, the differences(2304) may be stored along with a single copy of the root VMM's instanceof the particular VM to effect a checkpoint of every different instanceof the particular VM in the logical tree topology.

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readabletransmission medium or a computer readable storage medium. A computerreadable storage medium may be, for example, but not limited to, anelectronic, magnetic, optical, electromagnetic, infrared, orsemiconductor system, apparatus, or device, or any suitable combinationof the foregoing. More specific examples (a non-exhaustive list) of thecomputer readable storage medium would include the following: anelectrical connection having one or more wires, a portable computerdiskette, a hard disk, a random access memory (RAM), a read-only memory(ROM), an erasable programmable read-only memory (EPROM or Flashmemory), an optical fiber, a portable compact disc read-only memory(CD-ROM), an optical storage device, a magnetic storage device, or anysuitable combination of the foregoing. In the context of this document,a computer readable storage medium may be any tangible medium that cancontain, or store a program for use by or in connection with aninstruction execution system, apparatus, or device.

A computer readable transmission medium may include a propagated datasignal with computer readable program code embodied therein, forexample, in baseband or as part of a carrier wave. Such a propagatedsignal may take any of a variety of forms, including, but not limitedto, electro-magnetic, optical, or any suitable combination thereof. Acomputer readable transmission medium may be any computer readablemedium that is not a computer readable storage medium and that cancommunicate, propagate, or transport a program for use by or inconnection with an instruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention are described above with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

It will be understood from the foregoing description that modificationsand changes may be made in various embodiments of the present inventionwithout departing from its true spirit. The descriptions in thisspecification are for purposes of illustration only and are not to beconstrued in a limiting sense. The scope of the present invention islimited only by the language of the following claims.

1. A method of administering a plurality of virtual machines (‘VMs’) ina distributed computing environment, the distributed computingenvironment comprising a plurality of hosts, one or more of the hostsexecuting a virtual machine monitor (‘VMM’), each VMM supportingexecution of one or more VMs, the method comprising: assigning, by a VMMmanager, the VMMs of the distributed computing environment to a logicaltree topology, including assigning one of the VMMs as a root VMM of thetree topology; and executing, amongst the VMMs of the tree topology, abroadcast operation, including: pausing, by the root VMM, execution ofone or more VMs supported by the root VMM; sending, by the root VMM, toother VMMs in the tree topology, a message indicating a pending transferof the paused VMs; and transferring the paused VMs from the root VMM tothe other VMMs.
 2. The method of claim 1, further comprising: executing,upon receipt by the other VMMs, the transferred VMs; and resuming, bythe root VMM, execution of the paused VMs.
 3. The method of claim 1,further comprising: after transfer of the VMs, not resuming execution ofthe paused VMs by the root VMM; and executing, upon receipt by the otherVMMs, the transferred VMs.
 4. The method of claim 1, further comprising:storing, upon receipt by the other VMMs, the transferred VMs withoutexecuting the transferred VMs.
 5. The method of claim 1, whereinexecuting the broadcast operation further comprises executing thebroadcast operation periodically at predefined intervals without userintervention.
 6. The method of claim 1, wherein executing the broadcastoperation further comprises executing the broadcast operation on-demandresponsive to a user request.
 7. The method of claim 1, whereinexecuting the broadcast operation further comprises executing thebroadcast operation, automatically without user intervention, independence upon an execution policy and responsive to an event specifiedin the execution policy.
 8. The method of claim 1, wherein thedistributed computing environment comprises a parallel computer, theparallel computer comprising a plurality of compute nodes, each of thecompute nodes comprising one of the plurality of hosts and executing atleast one of the VMMs, each compute node further comprising a pluralityof communications adapters, each communications adapter configured tocouple the compute node to other compute nodes for data communicationsand to one of a plurality of data communications networks.
 9. The methodof claim 8, wherein one of the plurality of data communications networkscomprises a multi-dimensional, point-to-point network.
 10. The method ofclaim 8, wherein one of the plurality of data communications networkscomprises a global combining network. 11-20. (canceled)